1. Field of the Invention
The present invention relates to a method for extracting an object region in a motion picture, which can rapidly extract the object region by using a degree of a motion of an object.
2. Background of the Related Art
These days, a technique of tracking an object in a motion picture has been generally applied in a field of image search of a monitoring camera, and in an object-based coding field such as compression of image communication or MPEG-4.
In the monitoring camera environment, if an instantaneous motion is captured in a situation that no moving object exists, an object is considered to be appeared, and then an object region is extracted and tracked in a motion-generated region. Since the background is usually fixed and motion is rarely found in the monitoring camera environment, the object can be relatively easily tracked using motion information.
Recently, an object-based coding method has been focused to perform image communication with a small amount of data, or change of the background. A standard coding method for these purposes is MPEG-4. In addition, a lot of attempts have been made to embody efficient data networking by coding an object region and a background region based on different methods.
As described above, in the image communication environment, while an image is being displayed, most of moving objects appear on a screen. And a ratio of the moving object to the screen size is greater than a ratio of the moving object to the monitoring camera. Thus, in the image communication environment, it is difficult to extract or track the object region by using the motion information.
Accordingly, a variety of methods for extracting or tracking the object region have been suggested.
As one of them, there is a method to initiate an object region by using motion information and edge information, and extract the object region as follows. Firstly, the object region of a previous frame is shifted in a predetermined direction, and overlapped with edges of a current frame. A direction of the largest overlap region is deemed as a moving direction, and the overlapped edges are presumed to be part of edges of a new object region. Thereafter, a difference between the previous frame and the current frame is obtained, and an edge region of the current frame in the difference-generated region is also presumed to be part of edges of the object region. The edge regions obtained through the two methods are ORed, to obtain the object region of the current frame.
However, the aforementioned method fails to obtain a precise edge in a background image having complicated edges.
There is another method to compose an object region by dividing a given image into regions by colors and merging them again. Here, sub regions are divided according to a Watershed method, and the regions of similar motion information and color information can be merged on the basis of similarity in motions and colors. In addition, the regions can be merged by merely using the color information. However, such an image division/merging based algorithm generally requires a long processing time, and thus is hard to be applied to a real time object tracking algorithm. In the case that a user wears clothes of complicated patterns, the regions are difficult to merge by colors.
As still another method, there is also provided a method to extract an initial object region with the help of the user, and track how the extracted region is moved by time. However, it also takes an extended period of time, and thus fails to apply itself in a real time processing.
A general method for extracting an object region from a motion picture will now be explained in more detail. FIG. 1 is a concept view illustrating the method for extracting the object region from the motion picture. Referring to FIG. 1, the method for extracting the object region includes a step for extracting an initial object region (step 111) and a step for tracking the extracted object region (step 121). Here, step 111 extracts the object region from a previous frame by using an automatic or semi-automatic algorithm, without any information on the object region.
Step 121 tracks the object region of a current frame on the basis of the object region extracted from the previous frame. It uses a faster algorithm than the initial object region extraction.
On the other hand, an object region re-modeling step (step 141) may be used in addition to the two essential steps, namely steps 111 and 121. When the object region is tracked for a long time, errors generated during the tracking process can be accumulated and increased. In order to prevent increase of the errors, the re-modeling process must be performed periodically or each time when a specific condition is met. For this, an additional step (step 131) for providing a periodical value or specific condition and judging it can be added.
A step (step 151) for refining the object region can be further included. Step 151 refines and modifies an imprecise boundary of the object region extracted according to the motion of the object.
The above-described methods are used for motion pictures obtained under an artificially well-formed lighting, such as news data.
However, these methods are not useful for a real environment such as the image communication due to the following reasons. That is, problems may occur because of noise of lighting itself or property of image obtaining device.
The image communication is mostly executed indoors, and a fluorescent light is generally used as an interior illumination. As publicly known, the fluorescent light is serious in flickering. Even though such flickering is not sensed by human eyes, when a photographed data is analyzed, a difference is generated between two temporally different frames even in a non-motion region due to the illumination property of the fluorescent light. The difference gets more serious in an edge region. In addition, a PC camera has lower image quality than a camcorder. Thus, noise may be generated in the whole images, especially, the lighting may be changed by motion of the user.
On the other hand, the more the object moves, the bigger difference the two frames have in a corresponding position, and the less the object moves, the smaller difference the two frames have. The two frames have a difference even in a non-motion position. The difference may be bigger than the difference generated in a boundary of the small motion object, and smaller than the difference generated in a boundary of the many motion objects.
Accordingly, when it is presumed that the difference is generated when the difference is big to remove noise due to technical problems, the small motion may be missed. When the small motion is intended to be found, noise of the background image is also detected. As a result, there are strong demands for extracting the object region of the current frame by distinguishing the motion of the object.